We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named...
Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masas...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...
Estimating the conditional mean of an inputoutput relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution ...
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density esti...
Laura M. Smith, Matthew S. Keegan, Todd Wittman, G...
Accurately evaluating statistical independence among random variables is a key element of Independent Component Analysis (ICA). In this paper, we employ a squared-loss variant of ...
We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estim...
Anupam Gupta, John D. Lafferty, Han Liu, Larry A. ...
In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...
We present a method for initialising the K-means clustering algorithm. Our method hinges on the use of a kd-tree to perform a density estimation of the data at various locations. ...
We consider multivariate density estimation with identically distributed observations. We study a density estimator which is a convex combination of functions in a dictionary and ...
We present a novel framework for efficiently computing the indirect illumination in diffuse and moderately glossy scenes using density estimation techniques. Many existing global...
Robert Herzog, Vlastimil Havran, Shin-ichi Kinuwak...